Some 45,000 children, including 20,000 Filipino infants die annually. Infant mortality rates per million live births have increased by 35% between 2020 and 2023, and 25% since 2018.
In the light of day, after a very late and rough night (I was quite sick while writing last night - literally, and not just in the face of this horrific data - self-treating and will be ok) I updated the report to include some annual infant death data to give a context of infant death patterns.
We must not forget if 2000 children died from an external assault, then there must be magnitudes more who are harmed, but not to the point of being killed.
Please share this. I hope that people working in the medical and legislative areas, and with churches would be concerned by this data (why aren't the government looking critically at their own data?!?) and would push for investigation.
PSA didn't publish this type of date earlier than 2020, so I can't see the death breakdown before 2020. This data would be needed via a FOI request - if they would respond.
Tragic! Can you find out how many Children died in The Philippines after being given Tamiflu = Oseltamivir and how many doses are sold?
Because patents don't apply in that country and some others, generic versions, as sold by The Wellness Company, are causing Sudden Infant Deaths that won't be sent to US FAERS.
Hi Sally, the Deadly generic Tamiflu (oseltamivir phosphate as sold by TWC) prices in the Philippines can vary, but a single 75mg tablet typically costs around ₱110.75 (approximately $2 USD).
I did a quick google and found numerous websites selling Oseltamivir and a Philippines government department spending 3,696,000.00 in July 2023. Thet got the drug from "L. MEYERF PHARMA, INC.- for the Procurement of
In Mexico "Oseltamivir increased mortality risk in the general population (HR=1.72, 95 % CI: 1.61-1.84), ambulatory (HR=4.79, 95 % CI: 4.01-5.75), non-critical (HR=2.05, 95 % CI: 1.88-2.23), and pregnancy (HR=8.35, 95 % CI: 1.77-39.30); as well as hospitalized (HR=1.13, 95 % CI: 1.01-1.26) and critical patients (HR=1.22, 95 % CI: 1.05-1.43) after propensity score-matching."
Another drug that clearly should be banned! I will try to search further if there are any local news surrounding this drug.
Local popular treatments for flu include calamansi (Filino lime) with honey, and a turmeric and ginger based drink that they call "salabat". Surely safer and more effective, without side effects.
Nothing to do with Philippines but everything to do with it. My niece lives in upstate New York. She's not anti-vaxxer but smells a rat and sees that kids don't look healthy. Her pediatrician is alarmed that she declines all vaccines.
Her husband who usually gets his way opines that the good doc "knows more" and should be obeyed. Thankfully my niece is holding holy-ground. I suggested that the good doc "knows more", means well but that everyone has a blind spot and we should question.
Fearing the M.D. w/blind trust in "authorities" goes to child welfare agencies I told my niece to stave off by demanding access to: formula changes, quality control changes, chain of custody considering import/export factors and independent critical peer reviews.
Of course the good doctor doesn't understand the mentality of patient due diligence but by not out right refusing he can't report negligence-abuse and giving him a worthy but uncompilable due diligence causing a bureaucratic stalemate. He'd never be able to refer her to independent-critical research.
The most troubling aspect is not what the medical physician community is doing to others but they eagerly do it for their own families.
The Philippines reaction throughout the plandemic, and degree of their lockdown mandates (I would say one of the harshest on the planet), made this expectation crystal clear as to what they were shooting for to me.
The Rockefeller-established "Western medicine" system has done a very good job of deleting medical common sense in the Filipino sheep, apart from the Euro and American sheep, of course.
SuperSally, you should have a Tiktok account. This article's title, can't find it via Google. I needed to enter Substack to successfully search for it. (don't bother with fakebook/meta and other strongly cabal-controlled platforms).
RSV vaccines are not routinely offered in the Philippines, except in a few very large hospitals. It self pay and regular folk won't be able to afford this.
Pregnancy vaccines typically offered here include DTAP (for tetanus) and flu shots. I have heard of mothers being offered others as well.
Vitamin K is offered but not mandated.
I suspect that the rising infant mortality in 2022 and 2023 is related to overall poorer maternal health. Nearly all children aged 12 and up, and nearly all working age women (and their partners) received Covid-19 jabs of some type or other. The reproductive impacts of these jabs are having impact.
Makes me wonder. The WHO provides these vaccines to developing countries. Do these places have high infant and pediatric mortality prior to the WHOs interventions, or because of these interventions?
Give toxic concoctions to infants and children with marginal nutrition and outcomes could be even worse…
Data has to be adjusted for the entire year. The days that account for 28 days to end of any month (on average) have to be calculated based on calendar months. ie. Annually we have 1 month with 28 days = 1, 4 months with 30 days = 3x4 = 12 days, 7 months with 31 days = 4x7=28 days. Add all days together = 1+12+28 = 41 days / 12 months = average of 3.417 days to account for the days between 28 and the last day of any month. This is then the divisor for the reported adverse events to come up with a daily average adverse events between day 28 and the end of any month. Further average days in a month in any year = 30.4167 which is why there is a 30+ days time range. That 30.4167 becomes the divisor for monthly deaths to arrive an average daily death in any month. Each data, other than the first 7 days which are reported by day, has to be adjusted to represent an average daily number of deaths for that specific period so that all data is directly comparable.
Thx for asking, nevertheless it should be self-explaining.
First time range means at day of date of injection (or maybe "during first 24h"),
2nd range, named "1 day" means during Day1 (after date of inj /resp after first 24h) until start of Day2 .... and so on.
The Range titled "28th Day to 1 month" has got that special name because change of frame from weekly durations to monthly durations.
In consequence we see the last period named with "11 months" , meaning time range from end of 11th month to end of 12 month.
The figures/numbers tells us by their own. The blindest Physicians/ med statistics would have recognized a catastrophe, if the interpretation of time frame like SuperSally made would have been right.
The time frames came with the original data from Philippines Statistics Office, and the categories supplied with that data. I plotted the original unmodified data and categories in the first graph.
However, to make the time frames comparable, and meaningful, I had to convert all data to deaths per day. The deaths for the first 7 days are deaths per day. Then the data changes to deaths per week, and I divide that data by 7 to give average deaths per week. Then the data changes to 28 to last of the month, which is 3.4 days and which becomes the divisor, and the data is average deaths between 28 and last day of any month. Then finally the data changes to monthly and is divided by average days per month in any year, and is monthly average daily deaths.
I have not seen such a breakdown of data from any other statistics offices in the region. This presentation of data is unique and gives an unsurpassed opportunity to assess the impacts of the 1 month well child visit (which is the only rational explanation) for these spiking deaths.
I can only wonder (not just for this, but for all the other assessments I have been doing) why the government does not look at its own data!
Hello Sally, please read the conversation with User @Zonder Reden
The Government and experts didnt recognize because you misunderstand this time range. “28th-1 month” means “from day 29 (following injection) till ca day 60” .
This seems crystal clear to me. Sorry, you have to correct your point of view here in.
No it's not Udo! The interval does not show from Day 28 till 60 days later from Start. The interval in which the anomaly is observed very clearly shows from Day 28 from Start till EOM that SAME MONTH from Start, and not between Day 28 and 60 days later. Supersally did a very good job. Respect.
Hello Udo, thanks for your reply. It may not be self-explanatory to many readers, that's why I asked. In any case, the problem lies mostly with the lack of consistency in wording, plus the lack of prepositions, plus the fact that the timespans change in the graph - these jump from days (in the first week from Start) to periods of random length (in the 3 weeks after Start) and to months (in the three weeks after Start and the rest of the year). It's a lousy graph in that sense, and very hard to understand. But, apparently, it does so because it aims to show the INTERVALS in which the anomaly is peaking.
And you are right, this does indeed look like a catastrophy that cannot be dismissed! And it should not.
Here we go:
In the Start of the graph the preposition "UNDER" is used with 1 Day. This is correct, and it makes the interpretation of the Start entry clear.
However, the prepositions "AFTER" and "WITHIN" should have been typed from thereon, to make clear exactly when the anomaly took place, and what its actual timeframe was. Take a look:
AFTER 2 days,
AFTER 3 days, etc until
AFTER 7 days, and then suddenly it goes to random length intervals:
WITHIN 8 to 13 days
WITHIN 14 to 20 days, and then it even goes from numbers to words as well:
WITHIN 28 days to 1 month, and then it goes flat out to periods of one month:
AFTER 2 months,
AFTER 3 months, etc until
AFTER 11 months.
Therefore:
UNDER 1 day is properly used for Start - the preposition "UNDER" tells us exactly that in less than one day the anomaly hits.
Then we continue with the exact number of days: 1 day, 2 days, 3 days, 4 days, ... 7 days - but what is the preposition now?? It cannot be UNDER. With the preposition "AFTER" we would know exactly when the observation took place.
Then we jump to random length periods expressed in a variable number of days: 8 - 13 days, 14 - 20 days; but what is the preposition?? And on top of that, it also jumps to a mix of units: 28 days - 1 month. And here is where it goes awry. There should have been a proper preposition: "WITHIN" to indicate that the anomaly is observable WITHIN an INTERVAL of Day 28 from Start to 1 month from Start.
(Note that the term "1 month" should really be EOM. And it cannot be "30 days" or "31 days" because the nr of days in a month vary, so they foolishly substitute a varable for a precise number.)
And then it jumps to months: 2 months, 3 months ..etc until 11 months. (Notice that 2 months is in there! If your claim is correct, there would not be a 2 months entry; it would show the 3 months entry and continue from there.)
Therefore, from Start, only 1 month has passed - 30 or 31 days. This is the month with the weird mix of days, intervals and months. And then the graph continues with the periods of months timeframe. Note that it starts with 2 months, which is correct, since only 1 month has passed:
2 months (about 60 days from Start at this point)
3 months (about 90 days from Start at this point)
4 months
etc, ending with
11 months (365 days)
Each entry here requires the preposition "AFTER" to inform the user that the anomaly was observed in this period of time AFTER Start.
As you can see, after 2 months the anomaly is much weaker than in the INTERVAL of "28 days - 1 month". And after 3 months, it's even weaker, etc.
It's a horribly confusing graph, with numbers and words mixed up, and no consistency, but the maker tried to show the precise intervals in which the anomaly takes place. He really should have at least used proper prepositions to avoid any misinterpretations of his jumping timeframes. (Jumping timeframes is otherwise allowed in graphs for brevity, but there must be consistency. And clarity.)
Nota bene:
We can also deduce that the preposition "AFTER" must be used, and not "AT" due to the fact that the graph stops with 11 months; AFTER 11 months means the entire 30 days that follows we can expect the entered anomaly, and in that sense you are correct - it means the timespan of that following (no, I meant 'entire' month, not 'following' month). But that does not hold true for the INTERVALS!
Therefore, our lady's work is accurate, and she has shown a true horror in this awful vaxx program for newly born humans in the Phillipines.
so we agree about the critical time range, lets call it “during month Nr2” following injection, what is not perfect exactly named, but near enough to reality to work with.
That means the Lady wasnt right in that specific question, which is unfortunately the most exciting headline point.
But she should be able to correct & edit her article, otherwise there would remain a shadow on her work, too big for many readers to take serious notice of the important urgent content, still remaining.
We do not agree. But you may explain why not. Again, the highest peak of the anomaly is visible in a time period of 4 days - shortly after the second injection of the Hep B vax on Day 28 after Start, till EOM of the same first month of the baby's life - emphasis on the SAME FIRST MONTH - not the second month. In that specific and very short time interval the highest peak of dying is observed. If the baby is still alive after 4 days of the second injection on Day 28 of its life, its chances of dying go down, observable in month 2, all month long, and it goes down as time passes. That's why the graph changed to units of months, going on with month 2, because there are no sudden peaks to behold. The peak was observable in the FIRST MONTH only, shortly after the injection on Day 28 of the baby's life. according to the data plotted on this graph.
Supersally is correct about the specificity in increased death after the second injection in the short interval of 4 days. If the baby's risk of dying lasted an entire 30 days or more, the month 2 would not be entered on the graph, but it would be an INTERVAL between Day 28 from Start and EOM of month 2. But that is not the case. The risk is observed in exactly the timeframe between Day 28 and the few days that follow. That means that the second injection into the newborn has immediate and great impact - and that is catastrophic. There is no shadow on her work. The second dose is a killer dose.
I took the time to explain all this, because Supersally is exposing a true horror and it should not be detracted from by an erroneous misunderstanding of the graph, which misunderstanding is then published to boot. Two injections of Hep B, with the second one causing a major increase in death a mere few days after application. It's a true horror. Why do you want her to 'correct' her article, when the truth is right there. It's a pity.
Interview with Liz Gunn and Andrew Bridgen touches on what the problem might be. The HepB vaccine mandatorily given at birth (GM rDNA product) might load the gun, vaccines then start at 4 to 6 weeks which is exactly when the mortality patterns change. https://x.com/LizGunnNZ/status/1920998144386757081
In the light of day, after a very late and rough night (I was quite sick while writing last night - literally, and not just in the face of this horrific data - self-treating and will be ok) I updated the report to include some annual infant death data to give a context of infant death patterns.
We must not forget if 2000 children died from an external assault, then there must be magnitudes more who are harmed, but not to the point of being killed.
Please share this. I hope that people working in the medical and legislative areas, and with churches would be concerned by this data (why aren't the government looking critically at their own data?!?) and would push for investigation.
PSA didn't publish this type of date earlier than 2020, so I can't see the death breakdown before 2020. This data would be needed via a FOI request - if they would respond.
That would be fascinating to see. Could it be possible to have someone from an embassy apply for this info?
I have asked Dr friends to try to request this. Unfortunately public offices are currently not very responsive to FOI requests.
The govt offices are FOI (Full Of It) :P
Medical murder is real.
Tragic! Can you find out how many Children died in The Philippines after being given Tamiflu = Oseltamivir and how many doses are sold?
Because patents don't apply in that country and some others, generic versions, as sold by The Wellness Company, are causing Sudden Infant Deaths that won't be sent to US FAERS.
https://geoffpain.substack.com/p/tamiflu-deaths-and-injuries-case
Very little pharmacovigilance data is publicly available in the Philippines. Unfortunately, I can't access any data.
Given the cost, Tamiflu will typically be used by middle class and higher individuals who can afford to buy it.
Hi Sally, the Deadly generic Tamiflu (oseltamivir phosphate as sold by TWC) prices in the Philippines can vary, but a single 75mg tablet typically costs around ₱110.75 (approximately $2 USD).
Minimum daily salary in the Philippines is currently ₱640.00 a day. This means that regular wage earners cannot afford this med (if they want to eat)!
Hello Sally, The Philippines Health Insurance Commission covers the cost of Oseltamivir under its "Outpatient Drug Benefit Package".
I did a quick google and found numerous websites selling Oseltamivir and a Philippines government department spending 3,696,000.00 in July 2023. Thet got the drug from "L. MEYERF PHARMA, INC.- for the Procurement of
Oseltamivir under IB#2023-106"
Thank you for this update. Great to know.
Unfortunately, they don't break down deaths in a way that could let us know the contribution of this drug to excess mortality.
Epidemiology is low level science, so I look at the known mechanisms via epigenetics.
https://geoffpain.substack.com/p/tamiflu-autism-and-100-other-mental
Doubtful that Dr Peter McCollough and others could be aware of this....... would you let him / they know please ?
I can try to have this raised to them via our local contact in World Council for health.
In Mexico "Oseltamivir increased mortality risk in the general population (HR=1.72, 95 % CI: 1.61-1.84), ambulatory (HR=4.79, 95 % CI: 4.01-5.75), non-critical (HR=2.05, 95 % CI: 1.88-2.23), and pregnancy (HR=8.35, 95 % CI: 1.77-39.30); as well as hospitalized (HR=1.13, 95 % CI: 1.01-1.26) and critical patients (HR=1.22, 95 % CI: 1.05-1.43) after propensity score-matching."
https://pmc.ncbi.nlm.nih.gov/articles/PMC7898041/
Another drug that clearly should be banned! I will try to search further if there are any local news surrounding this drug.
Local popular treatments for flu include calamansi (Filino lime) with honey, and a turmeric and ginger based drink that they call "salabat". Surely safer and more effective, without side effects.
Thanks Sally, I saw market projections for Tamiflu and its generics tipped for nearly 1 Trillion US$ unless we can stop it.
Nothing to do with Philippines but everything to do with it. My niece lives in upstate New York. She's not anti-vaxxer but smells a rat and sees that kids don't look healthy. Her pediatrician is alarmed that she declines all vaccines.
Her husband who usually gets his way opines that the good doc "knows more" and should be obeyed. Thankfully my niece is holding holy-ground. I suggested that the good doc "knows more", means well but that everyone has a blind spot and we should question.
Fearing the M.D. w/blind trust in "authorities" goes to child welfare agencies I told my niece to stave off by demanding access to: formula changes, quality control changes, chain of custody considering import/export factors and independent critical peer reviews.
Of course the good doctor doesn't understand the mentality of patient due diligence but by not out right refusing he can't report negligence-abuse and giving him a worthy but uncompilable due diligence causing a bureaucratic stalemate. He'd never be able to refer her to independent-critical research.
The most troubling aspect is not what the medical physician community is doing to others but they eagerly do it for their own families.
God bless your sister...... truely a mother who loves her children.
Some bits n pieces for your dear BIL: http://thinktwice.com
Dr Suzanne Humphries (former Nephrologist)
https://dissolvingillusions.com/graphs-images/
Childrens Health Defense - started by Bobby Kennedy when Mums approached him about their vax injured children.
childrenshealthdefense.org has a cornucopia of interviews and movies on: chd.tv
they are all really worthwhile sharing.....
https://live.childrenshealthdefense.org/chd-tv/movies/
The Philippines reaction throughout the plandemic, and degree of their lockdown mandates (I would say one of the harshest on the planet), made this expectation crystal clear as to what they were shooting for to me.
Thanks for making us aware of this data SuperSally. There is no way such a spike is not caused by some external event. It needs investigation.
Good find.
The Rockefeller-established "Western medicine" system has done a very good job of deleting medical common sense in the Filipino sheep, apart from the Euro and American sheep, of course.
SuperSally, you should have a Tiktok account. This article's title, can't find it via Google. I needed to enter Substack to successfully search for it. (don't bother with fakebook/meta and other strongly cabal-controlled platforms).
I wouldn't be surprised if it had something to do with RSV injections to mom or Beyfortus...?
RSV vaccines are not routinely offered in the Philippines, except in a few very large hospitals. It self pay and regular folk won't be able to afford this.
Pregnancy vaccines typically offered here include DTAP (for tetanus) and flu shots. I have heard of mothers being offered others as well.
Vitamin K is offered but not mandated.
I suspect that the rising infant mortality in 2022 and 2023 is related to overall poorer maternal health. Nearly all children aged 12 and up, and nearly all working age women (and their partners) received Covid-19 jabs of some type or other. The reproductive impacts of these jabs are having impact.
Madness to include Mercury
Makes me wonder. The WHO provides these vaccines to developing countries. Do these places have high infant and pediatric mortality prior to the WHOs interventions, or because of these interventions?
Give toxic concoctions to infants and children with marginal nutrition and outcomes could be even worse…
Malnutrition is a notable cause of death in PH.
Reminds me of the work of Dr Archie Kalokerinos. https://www.scribd.com/document/408500837/eBook-Vitamin-C-Archie-Kalokerinos-Every-Second-Child
The WHO provided pentavalent also includes aluminium and thiomersal. https://www.seruminstitute.com/product_ind_pentavac.php
Find a none/less/ vaccinated control group of infants!
there should be any population on earth remaining without such crazy increased vaccination schedule ? ;
the 2nd necessarity would be the implemention of a statistic reporting system there!?
Maybe an alternative way of comparison could be successful:
Historic figures from populations with tradition on statistics from times, when there were not such vaccination schedules implemented yet.
But even any comparison with data of other populations could be helpful to produce some new knowledge
Numbers of time range (28th to 1 month) remain significant hightend even when considered correctly as a 1-Month lasting time range!
The differences between year 2020 and each other year seems significant, too.
The shown differences concerning special causes of death between Month 1 and Month 2
seems to be highly significant,
including very significant increases after 2020 till 2023 !!
all of these 3 findings are in the need of explainations. URGENT, YES.
Like … injected poisons???
Hello Sally, it seems your most exciting point isnt any one!
You just misinterpreted the text "28th day to 1 months" as a 4 days time range;
but it is meant a 30+ days time range.
Data has to be adjusted for the entire year. The days that account for 28 days to end of any month (on average) have to be calculated based on calendar months. ie. Annually we have 1 month with 28 days = 1, 4 months with 30 days = 3x4 = 12 days, 7 months with 31 days = 4x7=28 days. Add all days together = 1+12+28 = 41 days / 12 months = average of 3.417 days to account for the days between 28 and the last day of any month. This is then the divisor for the reported adverse events to come up with a daily average adverse events between day 28 and the end of any month. Further average days in a month in any year = 30.4167 which is why there is a 30+ days time range. That 30.4167 becomes the divisor for monthly deaths to arrive an average daily death in any month. Each data, other than the first 7 days which are reported by day, has to be adjusted to represent an average daily number of deaths for that specific period so that all data is directly comparable.
See my explanation to Udo. Your work is solid, and deserves compliments. :-)
Sally, you did focus too much on fine details. You might be not wrong in such details particularly.
But by focusing on all the small trees you forgot about the main edges of the whole forest ;- )
Can you please support with an accurate explanation why "28 days to 1 month" is 30+ days to you? (Your statement is meaningless right now.)
Thx for asking, nevertheless it should be self-explaining.
First time range means at day of date of injection (or maybe "during first 24h"),
2nd range, named "1 day" means during Day1 (after date of inj /resp after first 24h) until start of Day2 .... and so on.
The Range titled "28th Day to 1 month" has got that special name because change of frame from weekly durations to monthly durations.
In consequence we see the last period named with "11 months" , meaning time range from end of 11th month to end of 12 month.
The figures/numbers tells us by their own. The blindest Physicians/ med statistics would have recognized a catastrophe, if the interpretation of time frame like SuperSally made would have been right.
The time frames came with the original data from Philippines Statistics Office, and the categories supplied with that data. I plotted the original unmodified data and categories in the first graph.
However, to make the time frames comparable, and meaningful, I had to convert all data to deaths per day. The deaths for the first 7 days are deaths per day. Then the data changes to deaths per week, and I divide that data by 7 to give average deaths per week. Then the data changes to 28 to last of the month, which is 3.4 days and which becomes the divisor, and the data is average deaths between 28 and last day of any month. Then finally the data changes to monthly and is divided by average days per month in any year, and is monthly average daily deaths.
I have not seen such a breakdown of data from any other statistics offices in the region. This presentation of data is unique and gives an unsurpassed opportunity to assess the impacts of the 1 month well child visit (which is the only rational explanation) for these spiking deaths.
I can only wonder (not just for this, but for all the other assessments I have been doing) why the government does not look at its own data!
Hello Sally, please read the conversation with User @Zonder Reden
The Government and experts didnt recognize because you misunderstand this time range. “28th-1 month” means “from day 29 (following injection) till ca day 60” .
This seems crystal clear to me. Sorry, you have to correct your point of view here in.
No it's not Udo! The interval does not show from Day 28 till 60 days later from Start. The interval in which the anomaly is observed very clearly shows from Day 28 from Start till EOM that SAME MONTH from Start, and not between Day 28 and 60 days later. Supersally did a very good job. Respect.
Hello Udo, thanks for your reply. It may not be self-explanatory to many readers, that's why I asked. In any case, the problem lies mostly with the lack of consistency in wording, plus the lack of prepositions, plus the fact that the timespans change in the graph - these jump from days (in the first week from Start) to periods of random length (in the 3 weeks after Start) and to months (in the three weeks after Start and the rest of the year). It's a lousy graph in that sense, and very hard to understand. But, apparently, it does so because it aims to show the INTERVALS in which the anomaly is peaking.
And you are right, this does indeed look like a catastrophy that cannot be dismissed! And it should not.
Here we go:
In the Start of the graph the preposition "UNDER" is used with 1 Day. This is correct, and it makes the interpretation of the Start entry clear.
However, the prepositions "AFTER" and "WITHIN" should have been typed from thereon, to make clear exactly when the anomaly took place, and what its actual timeframe was. Take a look:
AFTER 2 days,
AFTER 3 days, etc until
AFTER 7 days, and then suddenly it goes to random length intervals:
WITHIN 8 to 13 days
WITHIN 14 to 20 days, and then it even goes from numbers to words as well:
WITHIN 28 days to 1 month, and then it goes flat out to periods of one month:
AFTER 2 months,
AFTER 3 months, etc until
AFTER 11 months.
Therefore:
UNDER 1 day is properly used for Start - the preposition "UNDER" tells us exactly that in less than one day the anomaly hits.
Then we continue with the exact number of days: 1 day, 2 days, 3 days, 4 days, ... 7 days - but what is the preposition now?? It cannot be UNDER. With the preposition "AFTER" we would know exactly when the observation took place.
Then we jump to random length periods expressed in a variable number of days: 8 - 13 days, 14 - 20 days; but what is the preposition?? And on top of that, it also jumps to a mix of units: 28 days - 1 month. And here is where it goes awry. There should have been a proper preposition: "WITHIN" to indicate that the anomaly is observable WITHIN an INTERVAL of Day 28 from Start to 1 month from Start.
(Note that the term "1 month" should really be EOM. And it cannot be "30 days" or "31 days" because the nr of days in a month vary, so they foolishly substitute a varable for a precise number.)
And then it jumps to months: 2 months, 3 months ..etc until 11 months. (Notice that 2 months is in there! If your claim is correct, there would not be a 2 months entry; it would show the 3 months entry and continue from there.)
Therefore, from Start, only 1 month has passed - 30 or 31 days. This is the month with the weird mix of days, intervals and months. And then the graph continues with the periods of months timeframe. Note that it starts with 2 months, which is correct, since only 1 month has passed:
2 months (about 60 days from Start at this point)
3 months (about 90 days from Start at this point)
4 months
etc, ending with
11 months (365 days)
Each entry here requires the preposition "AFTER" to inform the user that the anomaly was observed in this period of time AFTER Start.
As you can see, after 2 months the anomaly is much weaker than in the INTERVAL of "28 days - 1 month". And after 3 months, it's even weaker, etc.
It's a horribly confusing graph, with numbers and words mixed up, and no consistency, but the maker tried to show the precise intervals in which the anomaly takes place. He really should have at least used proper prepositions to avoid any misinterpretations of his jumping timeframes. (Jumping timeframes is otherwise allowed in graphs for brevity, but there must be consistency. And clarity.)
Nota bene:
We can also deduce that the preposition "AFTER" must be used, and not "AT" due to the fact that the graph stops with 11 months; AFTER 11 months means the entire 30 days that follows we can expect the entered anomaly, and in that sense you are correct - it means the timespan of that following (no, I meant 'entire' month, not 'following' month). But that does not hold true for the INTERVALS!
Therefore, our lady's work is accurate, and she has shown a true horror in this awful vaxx program for newly born humans in the Phillipines.
It's truly horrendous and more than a little sad.
With kind regards. -ZR
so we agree about the critical time range, lets call it “during month Nr2” following injection, what is not perfect exactly named, but near enough to reality to work with.
That means the Lady wasnt right in that specific question, which is unfortunately the most exciting headline point.
But she should be able to correct & edit her article, otherwise there would remain a shadow on her work, too big for many readers to take serious notice of the important urgent content, still remaining.
We do not agree. But you may explain why not. Again, the highest peak of the anomaly is visible in a time period of 4 days - shortly after the second injection of the Hep B vax on Day 28 after Start, till EOM of the same first month of the baby's life - emphasis on the SAME FIRST MONTH - not the second month. In that specific and very short time interval the highest peak of dying is observed. If the baby is still alive after 4 days of the second injection on Day 28 of its life, its chances of dying go down, observable in month 2, all month long, and it goes down as time passes. That's why the graph changed to units of months, going on with month 2, because there are no sudden peaks to behold. The peak was observable in the FIRST MONTH only, shortly after the injection on Day 28 of the baby's life. according to the data plotted on this graph.
Supersally is correct about the specificity in increased death after the second injection in the short interval of 4 days. If the baby's risk of dying lasted an entire 30 days or more, the month 2 would not be entered on the graph, but it would be an INTERVAL between Day 28 from Start and EOM of month 2. But that is not the case. The risk is observed in exactly the timeframe between Day 28 and the few days that follow. That means that the second injection into the newborn has immediate and great impact - and that is catastrophic. There is no shadow on her work. The second dose is a killer dose.
I took the time to explain all this, because Supersally is exposing a true horror and it should not be detracted from by an erroneous misunderstanding of the graph, which misunderstanding is then published to boot. Two injections of Hep B, with the second one causing a major increase in death a mere few days after application. It's a true horror. Why do you want her to 'correct' her article, when the truth is right there. It's a pity.
Interview with Liz Gunn and Andrew Bridgen touches on what the problem might be. The HepB vaccine mandatorily given at birth (GM rDNA product) might load the gun, vaccines then start at 4 to 6 weeks which is exactly when the mortality patterns change. https://x.com/LizGunnNZ/status/1920998144386757081